Ying Zhao, Ph.D. Research Professor Information Sciences Department Naval Postgraduate School 1 University Way Monterey, CA 93943 yzhao at nps.edu

 

  Short Bio  Curriculum Vitae NPS Students Theses Advised

  Publications and Presentations

1. Zhao, Y., Mata, G., & Zhou, J. (2025). Discovering Root Causes of Risks for Warfighting Systems & Human Factors Integration (WHI). Poster Presentation at the 9^th the Annual Conference for Naval Applications of Machine Learning, 24-27 February, 2025, San Diego.

2.       Zhao, Y. (2024). Addressing Data Gaps in Arctic Ocean and Seabed Conditions that Affect Naval Operations and Planning. Poster presentation at the at the 2024 American Geophysical Union (AGU) Fall Meeting, 9-13 December 2024, Washington, D.C. https://agu24.ipostersessions.com/?s=D3-8D-D2-42-37-4C-C0-9B-7D-9E-97-D5-4A-39-06-B3

3.       Zhao, Y. (2024). Quantum Theoretic Values of Collaborative and Self-Organizing Agents in Forming a Hybrid Force.  In the 29th ICCRTS Proceedings, 24-26 September 2024 · London, UK.

4. Zhao Y. (2024). Oral Presentation at the ONR NEPTUNE Program Review.  Stanford Faculty Club, 439 Lagunita Drive, Stanford, CA. 6-7 November, 2024.

5.       Zhao, Y. (2024). Knowledge Graphs (KG) Assisted Variational Autoencoder (VAE) for Large-Scale Anomaly and Event Detection. ASONAM ‘24: Proceedings of the 2024 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. University of Calabria, Rende (CS), Italy, 2-5 September, 2024, https://link.springer.com/chapter/10.1007/978-3-031-78554-2_13

6.       Zhao, Y. (2024). Knowledge Graphs Assisted Variational Autoencoder (KG-VAE) for Large-Scale Anomaly and Event Detection. Poster presentation at the Knowledge Graphs and Ontologies in Intelligence, Defence and Security Symposium, 18 June, 2024, Cheltenham Racecourse, Evesham Rd, Cheltenham GL50 4SH, UK.

7.       Zhao, Y. (2024). ML/AI Assisted Anomaly and Event Detection for the Distributed Acoustic Sensing (DAS) Data, NAML 2024 presentation, March 13, 2024. San Diego.

8.       Zhao, Y. & MacKinnon, D. J. (2023). Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the Operating Forces of the U.S. Navy. Naval Engineers Journal, 135(4), Winter 2023. https://www.ingentaconnect.com/contentone/asne/nej/2023/00000135/00000004/art00027;jsessionid=3221ba0o3kdo6.x-ic-live-02

9.       Zhao, Y., & Zhou, C. (2023). Quantum Theoretic Values of Collaborative and Self-organizing Agents. ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining November 2023. Pages 678-685. Kusadasi, Turkiye, November 6 - 9, 2023. https://dl.acm.org/doi/10.1145/3625007.3627509

10.   Zhao, Y., Mata, G. & Zhou, C. (2023). Self-organizing and Load-Balancing via Quantum Intelligence Game for Peer-to-Peer Collaborative Learning Agents and Flexible Organizational Structures. In: Arai, K. (eds) Intelligent Computing. SAI 2023. Lecture Notes in Networks and Systems, vol 711. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-031-37717-4_33 (presentation, interview).

11.   Zhou, C.C., Zhao, Y. (2023). Crowd-Sourcing High-Value Information via Quantum Intelligence Game. In: Arai, K. (eds) Intelligent Computing. SAI 2023. Lecture Notes in Networks and Systems, vol 711. Springer, Cham. https://doi.org/10.1007/978-3-031-37717-4_34.

12.   Zhao, Y. & MacKinnon, D. J. (2023). Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the Operating Forces of the U.S. Navy. In the proceedings of the 2023 Annual Acquisition Research Symposium, May, 2023, Monterey, CA.  https://dair.nps.edu/handle/123456789/4928 (presentation).

13.   Zhao, Y. (2023). Leverage AI to learn, optimize, and wargame (LAILOW) for strategic laydown and dispersal (SLD) of the operating forces of the U.S. Navy. Presentation to the 43th Soar Workshop, University of Michigan, Ann Arbor, June 14, 2023. (presentation)

14.   Zhao, Y. (2022). Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem. https://apps.dtic.mil/sti/pdfs/AD1189485.pdf

15.   Zhao, Y. (2022). Integrating Human Reasoning and Machine Learning for Causal Learning Applied to Defense Applications. Invited Talk at the at the Supsec 3rd workshop: AI for Supervision, September 19th, 2022, Inria Rennes, France. https://supsec.github.io/ (pdf)

16.   Zhao, Y., Hemberg, E., Derbinsky, N., Mata, G., and O’Reilly, U. (2022). Using domain knowledge in coevolution and reinforcement learning to simulate a logistics enterprise. GECCO '22. https://dl.acm.org/doi/10.1145/3520304.3528990

17.   Zhao, Y. (2022). NPS Foundation Interview. https://www.npsfoundation.org/faces-archive/faces-of-nps-17

18.   Zhao, Y. (2021). Developing A Threat and Capability Coevolutionary Matrix (TCCM) – Application to Shaping Flexible C2 Organizational Structure for Distributed Maritime Operations (DMO). In the Proceedings of 18th Annual Acquisition Research Symposium, Virtual, May, 2021.  https://dair.nps.edu/bitstream/123456789/4399/1/SYM-AM-21-092.pdf

19.   Zhao, Y., Hemberg, E., Derbinsky, N., Mata, G., and O’Reilly, U. (2021). Simulating a Logistics Enterprise Using an Asymmetrical Wargame Simulation with Soar Reinforcement Learning and Coevolutionary Algorithms. In 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion), July 10–14, 2021, Lille, France. ACM, New York, NY, USA, 9 pages.

20.   https://dl.acm.org/doi/10.1145/3449726.3463172

21.   Zhao, et al. (2021). Leverage artificial intelligence to Learn, Optimize, and Wargame (LAILOW) for Navy Ships.  In the Special Webinar Developing Artificial Intelligence in Defense Programs and Proceedings of  the 18^th Annual Acquisition Research Symposium, Virtual, March 3, 2021.  https://dair.nps.edu/handle/123456789/4396

22.   Zhao, Y., Nagy, B., Kendall , T. and Schwamm, R. (2020). Modeling A Multi-segment Wargame Leveraging Machine Intelligence and Event-Verb-Event (EVE) Structures. In Proceedings of AAAI Symposium on the 2nd Workshop on Deep Models and Artificial Intelligence for Defense Applications: Potentials, Theories, Practices, Tools, and Risks, November 11- 12, 2020, Virtual. http://ceur-ws.org/Vol-2819/session3paper3.pdf

23.   Zhao, Y. and Mata, G. (2020). Leverage artificial intelligence to learn, optimize, and win (LAILOW) for the marine maintenance and supply complex system. In the 2020 International Symposium on Foundations and Applications of Big Data Analytics (FAB 2020) in conjunction with the IEEE/ACM ASONAM, 7-10 December 2020, Virtual. https://ieeexplore.ieee.org/document/9381319

24.   Zhao Y. (2020) Deep analytics for management and cybersecurity of the national energy grid. In: Krzhizhanovskaya V. et al. (eds) Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12141. Springer, Cham. https://doi.org/10.1007/978-3-030-50426-7_23.

25.   Geldmacher, J., Yerkes, C., and Zhao, Y. (2020). Convolutional neural networks for feature extraction and automated target recognition in synthetic aperture radar images.  In Proceedings of AAAI Symposium on the 2nd Workshop on Deep Models and Artificial Intelligence for Defense Applications: Potentials, Theories, Practices, Tools, and Risks, November 11- 12, 2020, Virtual, http://ceur-ws.org/Vol-2819/session2paper4.pdf

26.   Dyer, C., Wood, B., Zhao, Y., and MacKinnon, D.J. (2020). Determining policy communication effectiveness: a lexical link analysis approach. Accepted by the 12th International Joint Conference on Knowledge Discovery and Information Retrieval (KDIR), 2-4 November, 2020.

27.   Zhao Y. and Zhou, Y. (2020). Link analysis to discover insights from structured and unstructured Data on COVID-19. In Proceedings of the11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB ’20), September 21–24, 2020, Virtual Event, USA. ACM, New York, NY, USA. https://doi.org/10.1145/3388440.3415990

28.   Zhao, Y. and Nagy, B. (2020). Modeling a multi-segment war game leveraging machine intelligence with EVE structures. Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131V (18 May 2020); https://doi.org/10.1117/12.2561855

29.   Zhao, Y. and Stevens, E. (2020).  Using lexical link analysis (LLA) as a tool to analyze a complex system and improve sustainment. Book chapter in Unifying Themes in Complex Systems X, Springer.  In Proceedings of AAAI Symposium on the 2nd Workshop on Deep Models and Artificial Intelligence for Defense Applications: Potentials, Theories, Practices, Tools, and Risks, November 11- 12, 2020, Virtual, http://ceur-ws.org/Vol-2819/session1paper3.pdf

30.   Zhao, Y. and Jones, L. (2020).  Integrating Human Reasoning and Machine Learning to Classify Cyber Attacks. Book chapter in Adversary-Aware Learning Techniques and Trends in Cybersecurity, edited by: Prithviraj Dasgupta, Joseph Collins, and Ranjeev Mittu, Springer Nature Switzerland AG. https://link.springer.com/chapter/10.1007/978-3-030-55692-1_8

31.   Zhao, Y. et al. (2019). Deep Models and Artificial Intelligence for Defense Applications (DMAIDA): Potentials, Theories, Practices, Tools and Risks. The 2019 Special Issue of AI Magazine (Volume 1 and Volume 2). Editors.  

32.   Zhao, Y., Gera, R., Halpin, Q., and Zhou, J. (2019). Visualization techniques for network analysis and link analysis. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-382-7, pages 561-568. DOI: 10.5220/0008377805610568, Vienna, Austria, September 17-19, 2019. Retrieved from https://www.insticc.org/Primoris/Resources/PaperPdf.ashx?idPaper=83778

33.   Zhao, Y. and Nagy, B. (2019). Causal learning in modeling multi-segment war game leveraging machine intelligence with EVE structures. A poster in the AAAI 2019 Fall Symposium. November 7–9, 2019. Arlington, VA.

34.   Zhao, Y., Kendall, K. and Schwamm, R. (2019).  Measures of effectiveness (MoEs) for MarineNet: A case study for a smart e-Learning organization. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, ISBN 978-989-758-382-7, pages 146-156. DOI: 10.5220/0008480701460156, Vienna, Austria, September 17-19, 2019. Retrieved from https://www.insticc.org/Primoris/Resources/PaperPdf.ashx?idPaper=84807

35.   Zhao, Y., C. Zhou, and Huang, S. (2019).  Theory and use case of game-theoretic lexical link analysis. In the proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Pages 717-720. Vancouver, Canada. August, 2019. Retrieved from https://dl.acm.org/doi/10.1145/3341161.3343706

36.   Zhao Y., Jones, J., and MacKinnon, D. (2019). Causal Learning Using Pair-wise Associations to Discover Supply Chain Vulnerability. In  Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-382-7, pages 305-309. DOI: 10.5220/0008070503050309, Vienna, Austria, September 17-19, 2019. Retrieved from https://www.insticc.org/Primoris/Resources/PaperPdf.ashx?idPaper=80705

37.   Zhao, Y. and Zhou, C.C. (2019). Collaborative Learning Agents (CLA) for Swarm Intelligence and Applications to Health Monitoring of System of Systems. In: Rodrigues J. et al. (eds) Computational Science – ICCS 2019, the 19th International Conference, Faro, Portugal, June 12–14, 2019. Lecture Notes in Computer Science, vol. 11538, pp. 706-718. Springer, Cham. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-22744-9_55

38.   Zhao, Y., Derbinsky, N., Wong, L., Sonnenshein, J. & Kendall, T. (2018). Continual and Real-time Learning for Modeling Combat Identification in a Tactical Environment. Accepted to the NIPS 2018 Workshop on Continual Learning, December 2-9, 2018, Montreal, Canada.  Retrieved from https://sites.google.com/view/continual2018/submissions

39.   Zhao, Y., Polk, A., Kallis, S., Jones, L., Schwamm, R., & Kendall, T. (2018). Big Data and Deep Models Applied to Cyber Security Data Analysis. In the technical report of the Association for the Advancement of Artificial Intelligence (AAAI), the 2018 Fall Symposium: Adversary-aware Learning Techniques and Trends in Cybersecurity (ALEC) of the AAAI Fall Symposium, October 18-19, 2018, Arlington, VA. Retrieved from http://ceur-ws.org/Vol-2269/

40.   Zhao, Y., Wu, R., Xi, M., Polk A., & Kendall, T. Big Data and Deep Learning Models for Automatic Dependent Surveillance Broadcast (ADS-B). In the technical report of the Association for the Advancement of Artificial Intelligence (AAAI), the 2018 Fall Symposium:  Reasoning and Learning in Real-World Systems for Long-Term Autonomy (LTA 2018). AAAI 2018 Fall Symposium. October 18-19, 2018, Arlington, VA, USA. Retrieved from http://rbr.cs.umass.edu/lta/papers/FSS-18_paper_56.pdf

41.   Zhao, Y. (2018).  Deep Models, Machine Learning and Artificial Intelligence Applications in National and International Security. Invited presentation at the Machine Learning, Data Analytics and Modeling (DATAM 2018, http://necsi.edu/events/CCS2018-satellite) – a satellite session at the Conference on Complex Systems (the http://ccs2018.web.auth.gr/), September 23-28, 2018, Thessaloniki, Greece.

42.   Zhao Y., Zhou, C. & Bellonio, J. (2018). New Value Metrics using Unsupervised Machine Learning, Lexical Link Analysis and Game Theory for Discovering Innovation from Big Data and Crowd-sourcing. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-330-8, ISSN 2184-3228, pages 327-334. DOI: 10.5220/0006959403270334, September 18-20, 2018, in Seville, Spain. Retrieved from http://www.scitepress.org/PublicationsDetail.aspx?ID=yednaU+deM4=&t=1

43.   Zhao, Y. & Zhou C. (2018). A Game-Theoretic Lexical Link Analysis for Discovering High-Value Information from Big Data. In the proceedings the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Barcelona, Spain, 28-31 Aug. 2018 (ASONAM 2018), page 621 – 625. Retrieved from https://ieeexplore.ieee.org/document/8508317.

44.   Zhao Y., Zhou, C. & Bellonio, J. (2018). Multilayer Value Metrics Using Lexical Link Analysis and Game Theory for Discovering Innovation from Big Data and Crowd-Sourcing. In the proceedings the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Barcelona, Spain, 28-31 Aug. 2018 (ASONAM 2018), page 1145 - 1151. Retrieved from https://ieeexplore.ieee.org/document/8508498.

45.   Zhao, Y. & Zhou, C. (2018), Data Sciences Meet Machine Learning and Artificial Intelligence:  A Use Case to Discover and Predict Emerging and High-Value Information from Business News and Complex Systems. Presentation at the 9^th International Conference on Complex Systems, the New England Complex Systems Institute, Boston, July 26, 2018.

46.   Zhao, Y. & Kendall, T. (2018). Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning Using the Naval Simulation System and Soar.  Presentation at the 2018 National Fire Control Symposium, 5 - 9 February 2018, Ft. Shafter, Honolulu, Oahu, Hawaii.

47.   Zhao Y., MacKinnon D.  & Zhou, C. (2017). Discovering High-Value Information from Crowdsourcing. In the proceedings the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Sydney, Australia,   31 July - 03 August, 2017 (ASONAM 2017). Retrieved from https://dl.acm.org/citation.cfm?doid=3110025.3121242

48.   Zhao^, Y., Mooren, E. & Derbinsky, N. (2017). Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning. In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-272-1, ISSN 2184-3228, pages 233-238. DOI: 10.5220/0006508702330238, Funchal, Portugal, Nov. 1-3, 2017.  Retrieved from http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=b0ttus7UXek=&t=1

49.   Salcido, R., Zhao, Y.  & Kendall, A. (2017). Analysis of Automatic Dependent Surveillance-Broadcast Data. In the technical report of the Association for the Advancement of Artificial Intelligence (AAAI), the 2017 Fall Symposium: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks. November 9-11, 2017, Arlington, Virginia. Retrieved from https://aaai.org/ocs/index.php/FSS/FSS17/paper/view/15996

50.   Halpin, Q., Zhao, Y.  & Kendall A. (2017). Using D3 to Visualize Lexical Link Analysis (LLA) and ADS-B Data. In the proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), the 2017 Fall Symposium: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks. November 9-11, 2017, Arlington, Virginia. Retrieved from https://aaai.org/ocs/index.php/FSS/FSS17/paper/view/16008

51.   Wu, R., Clarke, A. & Kendall A. (2017). A Framework Using Machine Vision and Deep Reinforcement Learning for Self-learning Moving Objects in a Virtual Environment.” in the proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), the 2017 Fall Symposium: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks. November 9-11, 2017, Arlington, Virginia. Retrieved from http://www.aaai.org/Library/Symposia/Fall/fs17-03.php

52.   Zhao, Y. & Zhao C. (2016). System Self-Awareness Towards Deep Learning and Discovering High-Value Information. In European Projects in Knowledge Applications and Intelligent Systems - Volume 1: EPS Lisbon 2016, ISBN 978-989-758-356-8, pages 160-179. DOI: 10.5220/0007901401600179  Ricardo J. Machado, Joao Sequeira, Hugo Placido de Silva and Joaquim Filipe (Eds.), Scitepress, Lisbon, Portugal.

53.   Zhao, Y., Mackinnon, D. J., Gallup, S. P., Billingsley, J. L. (2016). Leveraging Lexical Link Analysis (LLA) To Discover New Knowledge. Military Cyber Affairs, 2(1), 3.

54.   Zhao, Y. &  Zhou, C.  (2016). System Self-Awareness Towards Deep Learning and Discovering High-Value Information. In the Proceedings of the 7th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, Oct. 20-22, New York, USA. Page 109-116. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7777885

55.   Zhao, Y., Kendall, T. & Johnson, B. (2016). Big Data and Deep Analytics Applied to the Common tactical Air Picture (CTAP) and Combat Identification (CID). In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 443-449. DOI: 10.5220/0006086904430449, Porto, Portugal, November 9-11, 2016. Retrieved from http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=q+n3kcRRK1w=&t=1

56.   Zhao, Y., Mackinnon, D. J., Gallup, S. P. (2015). Big Data and Deep Learning for Understanding DoD data. Journal of Defense Software Engineering, Special Issue: Data Mining and Metrics.

57.   Zhao, Y., Gallup, S.P., and MacKinnon, D.J., (2014). Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal. In Proceedings for the 11th Annual Acquisition Research Symposium for Acquisition Management, Monterey, California, May 2014. https://apps.dtic.mil/sti/pdfs/ADA612929.pdf

58.   Zhao, Y., Brutzman, D. & MacKinnon, D.J. (2013). Improving DoD Energy Efficiency: Combining MMOWGLI Social Media Brainstorming with Lexical Link Analysis to Strengthen the Acquisition Process. In Proceedings of the Tenth Annual Acquisition Research Program. Monterey, CA: Naval Postgraduate School. May, 2013.

59.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2012). Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program Awareness. Acquisition Report NPS-AM-12-205. Retrieved from Naval Postgraduate School, Acquisition Research Program website: http://www.acquisitionresearch.net

60.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011). A Web Service Implementation for Large-Scale Automation, Visualization, and Real-Time Program-Awareness Via Lexical Link Analysis. Acquisition Report NPS-AM-11-186. Retrieved from Naval Postgraduate School, Acquisition Research Program website: http://www.acquisitionresearch.net

61.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2010). Towards real-time program awareness via Lexical Link Analysis. Acquisition Report NPS-AM-10-174. Retrieved from Naval Postgraduate School, Acquisition Research Program website: http://www.acquisitionresearch.net

62.   Zhao, Y., MacKinnon, D., & Gallup, S. (2012, June). Semantic and social networks comparison for the Haiti earthquake relief operations from APAN data sources using lexical link analysis. In Proceedings of the 17^th ICCRTS, International Command and Control, Research and Technology Symposium. Retrieved from http://www.dodccrp.org/events/17th_iccrts_2012/post_conference/papers/082.pdf

63.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011, September). System self-awareness and related methods for improving the use and understanding of data within DoD. Software Quality Professional, 13(4), 19–31. Retrieved from http://asq.org/pub/sqp/

64.   Zhao, Y., Gallup, S.P., and MacKinnon, D.J., (2014). Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal. In proceedings for the 11th Annual Acquisition Research Symposium for Acquisition Management, Monterey, California, May 2014. Retrieved from https://calhoun.nps.edu/bitstream/handle/10945/54618/NPS-AM-13-109.pdf

65.   Zhao, Y., Brutzman, D. & MacKinnon, D.J. (2013). Improving DoD Energy Efficiency: Combining MMOWGLI Social Media Brainstorming with Lexical Link Analysis to Strengthen the Acquisition Process. In Proceedings of the Tenth Annual Acquisition Research Program. Monterey, CA: Naval Postgraduate School. May, 2013.

66.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2012). Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program Awareness. Acquisition Report NPS-AM-12-205. Retrieved from https://calhoun.nps.edu/handle/10945/33852.

67.   Zhao, Y., MacKinnon, D., & Gallup, S. (2012, June). Semantic and social networks comparison for the Haiti earthquake relief operations from APAN data sources using lexical link analysis. In Proceedings of the 17th ICCRTS, International Command and Control, Research and Technology Symposium. Fairfax, Virginia, June 19–21, 2012. Retrieved from http://www.dodccrp.org/events/17th_iccrts_2012/post_conference/papers/082.pdf

68.   Zhao, Y., MacKinnon, D., Gallup, S. (2012, June).  Lexical Link Analysis and System Self-awareness: Theory and Practice.  Poster at the Cyber and Information Challenges 2012 Conference, Utica, NY from 6/11-15.  

69.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2012, May). Applications of Lexical Link Analysis Web Service for Large-scale Automation, Validation, Discovery, Visualization and Real-time Program-awareness. Presentation at the 9th Annual Acquisition Research Symposium, Monterey, California, May 16-17, 2012.

70.   Thomas, G. F., Stephens, K., Zhao, Y., Gallenson, A. (2012, March). Understanding Transactive Memory Systems in Inter-organizational Networks:  An Analysis of Haiti’s 2010 APAN Disaster Response Coordination.  Presentation in Sunbelt XXXII, or the International Sunbelt Social Network Conference is the official conference of the International Network for Social Network Analysis (INSNA), March 12,-18, 2012, Redondo Beach, CA.

71.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011, September). System self-awareness and related methods for improving the use and understanding of data within DoD. Software Quality Professional, 13(4), 19–31. Retrieved from http://asq.org/pub/sqp/

72.   Zhao, Y., MacKinnon, D., Gallup, S. (2011, June).  Lexical Link Analysis for the Haiti Earthquake Relief Operation Using Open Data Sources.  In Proceedings of the 16th ICCRTS, International Command and Control, Research and Technology Symposium, Québec City, Canada June 21–23, 2011. Retrieved from https://ntrl.ntis.gov/NTRL/dashboard/searchResults.xhtml?searchQuery=ADA547096.

73.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011, May). A web service implementation for large-scale automation, visualization and real-time program-awareness via lexical link analysis. In Proceedings of the Eighth Annual Acquisition Research Program. Monterey, CA: Naval Postgraduate School.

74.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011). A web service implementation for large-scale automation, visualization and real-time program-awareness via lexical link analysis (NPS-GSBPP-11-012). Monterey, CA: Naval Postgraduate School. Retrieved from https://calhoun.nps.edu/bitstream/handle/10945/33967/NPS-GSBPP-11-012.pdf.

75.   Zhao, Y., MacKinnon, D.J, & Gallup, S.P. (2011) System Self-awareness and Related Methods for Improving the Use and Understanding of Data within DoD. In American Society for Data Quality (ASQ), Volume 13, Issue 4, pp. 19-31, Sep 2011.  Retrieved from http://asq.org/qic/display-item/index.html?item=33878.

76.   Zhao, Y., Gallup, S., & MacKinnon, D. (2010). Towards real-time program awareness via lexical link analysis. In Proceedings of the Seventh Annual Acquisition Research. Acquisition Research Sponsored Report Series, NPS-AM-10-049, Monterey, CA: Naval Postgraduate School.  Retrieved from https://calhoun.nps.edu/bitstream/handle/10945/33482/NPS-AM-10-049.pdf.

77.   Zhao, Y., MacKinnon, D., Gallup, S., & Zhou, C. (2010).  Maritime domain awareness via agent learning and collaboration. In Proceedings of the 15th ICCRTS, International Command and Control, Research and Technology Symposium, Santa Monica, CA. Retrieved from http://www.dodccrp.org/events/15th_iccrts_2010/papers/106.pdf.

78.   Gallup, S., MacKinnon, D., Zhao, Y., Robey, J., & Odell, C. (2009). Facilitating decision making, re-use and collaboration: A knowledge management approach for system self-awareness. In Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2009) ISBN 978-989-674-013-9, pages 236-241. DOI: 10.5220/0002332002360241. Madeira, Portugal. Retrieved from http://www.dtic.mil/get-tr-doc/pdf?AD=ADA587494.

79.   Zhao, Y., MacKinnon, D., Gallup, S., & Zhou, C. (2010).  Maritime domain awareness via agent learning and collaboration. In Proceedings of the 15th ICCRTS, International Command and Control, Research and Technology Symposium, Santa Monica, CA, June 22-24, 2010. Retrieved from http://www.dodccrp.org/events/15th_iccrts_2010/papers/106.pdf.

80.   Zhou, C., Zhao, Y., & Kotak, C. (2009). The Collaborative Learning agent (CLA) in Trident Warrior 08 exercise. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 323-328. DOI: 10.5220/0002332903230328. Madeira, Portugal.  https://www.scitepress.org/Papers/2009/23329/23329.pdf.

81.   Zhao, Y., Wei, S., Oglesby, I., Zhou, C. (2009). Utilizing the Quantum Intelligence System for Drug Discovery (QIS D2) for anti-HIV and anti-cancer cocktail detection. In the Journal of Medical Chemical, Biological, & Radiological Defense (JMedCBR), Volume 7. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.465.8000&rep=rep1&type=pdf.

82.   Zhao, Y., Kotak, C. & Zhou C. (2008). Semantical machine understanding, in Proceedings of the 13th International Command and Control Research and Technology Symposium.  Washington, DC. Retrieved from http://www.dodccrp.org/events/13th_iccrts_2008/CD/html/papers/205.pdf

83.   Zhao, Y., Zhou, C.(2005). Large-scale drug function prediction by integrating QIS D2 and BioSpice. In Proceedings of IEEE Computational Systems. pp. 391-394. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1540654.

84.   Zhao, Y., Zhou, C. (2005). Drug characteristics prediction. In Proceedings of IEEE Computational Systems Bioinformatics Workshops. Stanford, CA: Stanford University. pp 395-398. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1540655.

85.   John, G. & Zhao, Y. (1997). Mortgage data mining. In Proceedings of the 1997 International Conference on Financial Engineering. New York.  Retrieved from https://ieeexplore.ieee.org/document/618942/.

86.   Zhao, Y. & Atkeson, C. (1996). Implementing projection pursuit. In the IEEE Transactions on Neural Networks, 7(2): p. 362-373. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=485672.

87.   Zhao, Y. (1995). Hierarchical mixtures of experts methodology applied to continuous speech recognition. In Proceedings of the 1995 International Conference on Acoustics, Speech, and Signal Processing, p. 3443-3446.

88.   Zhao, Y., Schwartz, R., Sroka, J. & Makhoul, J. (1994). Hierarchical Mixtures of Experts Methodology Applied to Continuous Speech Recognition. In Advances in Neural Information Processing Systems 7,  G. Tesauro and D.S. Touretzky and T.K. Leen (Eds.). San Mateo: Morgan Kaufmann Publishers. Retrieved from https://papers.nips.cc/paper/929-hierarchical-mixtures-of-experts-methodology-applied-to-continuous-speech-recognition.

89.   Zhao, Y., Schwartz, R. & Makhoul, J. (1993). Segmental neural net optimization for continuous speech recognition. In Advances in Neural Information Processing Systems 6, J.D. Cowan, G. Tesauro, and J. Alspector (Eds.). San Mateo: Morgan Kaufmann Publishers. Retrieved from https://papers.nips.cc/paper/763-segmental-neural-net-optimization-for-continuous-speech-recognition.pdf.

90.   Zhao, Y. & Atkeson, C. (1994). Projection pursuit learning: Some theoretical issues.  In Computational Learning Theory and Natural Learning Systems. S.J. Hanson, et al.(Eds.). Cambridge: MIT Press. Retrieved from https://dl.acm.org/citation.cfm?id=190821.

91.   Zhao, Y. & Atkeson, C. (1992). How projection-pursuit learning works in high dimensions. In Science of Artificial Neural Networks - Proc. of SPIE. Retrieved from https://www.spiedigitallibrary.org/conference-proceedings-of-spie/1710/0000/How-projection-pursuit-learning-works-in-high-dimensions/10.1117/12.140143.full?SSO=1.

92.   Zhao, Y. & Atkeson, C. (1991). Some approximation properties of projection pursuit learning networks. In Advances in Neural Information Processing Systems 4, J.E. Moody, S.J. Hanson, and R.P. Lippmann (Eds.). San Mateo: Morgan Kaufmann Publishers. Retrieved from https://papers.nips.cc/paper/493-some-approximation-properties-of-projection-pursuit-learning-networks.

 

NPS Student Theses Advised

·       Deondra I. Irby (6/2024). Apply Machine Learning and Sentiment Analysis to Assess the Health of Marine Corps Culture. Master of Science, Information Sciences and Defense Management, Naval Postgraduate School.

·       William J. Frazier (9/2022). Predictive Maintenance Using Machine Learning and Existing Data Sources. Master of Science, Computer Science, Naval Postgraduate School.

·       Kennedy, R. (9/2021). Applying Artificial Intelligence to Identify Cyber Spoofing Attacks against the Global Positioning System (GPS). Master of Science, Systems Engineering, Naval Postgraduate School.

·       Bruce A. Manuel Jr. (7/2021). Applying Information Design Principles and Methods to Operations in the Information Environment. Master of Science, Information Sciences and Defense Management, Naval Postgraduate School.

·       Gallagher, P. J. (9/2020). Predicting Marine Corps Retention Behavior with Machine Learning. Master of Science, Information Sciences and Defense Management, Naval Postgraduate School.

·       Geldmacher, J. (6/2020). Convolutional Neural Networks for Feature Extraction and Automated Target Recognition in Synthetic Aperture Radar Images. Master of Science, Information Sciences, Naval Postgraduate School.

·       Dyer, C. L. (6/2020). Determining Policy Communication Effectiveness: A Lexical Link Analysis Approach. Master of Science, Information Sciences, Naval Postgraduate School.

·       Stevens, E. J. (3/2020). Leveraging Predictive Analytics to Assess 7th Fleet Sustainment, Operations Research, Naval Postgraduate School.

·       Jones, J. P. (9/2019). MV-22 Supply Chain Agility: A Static Supply Chain Supporting A Dynamic Deployment. Master of Science. Information Sciences and Defense Management, Naval Postgraduate School.

·       Pollard, H. W. (9/2019). Improving Close Air Support Missions with the Use of Machine Learning Decision Aids. Master of Science, Systems Engineering, Naval Postgraduate School.

·       Deschler, P. J.  (9/2019). Leveraging Big Data Analytics (BDA) to Improve MV-22 Aviation Depot-Level Repairables (ADVLR) Maintenance.  Master of Science, Defense Management, Naval Postgraduate School.

·       Melkonian, C. G.  (9/2018). Compatibility Analysis of the Oracle Warehouse Management System with United States Marine Corps Warehouse Management Requirements.  Master of Science, Systems Engineering, Naval Postgraduate School.

·       Jones, L. M.  (9/2018). Big Data Analysis for Cyber Situational Awareness Analytic Capabilities. Master of Science, Information Sciences, Naval Postgraduate School.

·       Bellonio, J. K.  (9/2018). One in A Million: Finding the Innovative Idea. Information Sciences, Naval Postgraduate School.

·       Mooren, E. (3/2017). Reinforcement Learning Applications to Combat Identification. Master of Science, Information Sciences, Naval Postgraduate School.

·       Baumgartner, W. (6/2016). Big Data Technologies and Their Potential Benefits Toward Combat Identification in Integrated Air and Missile Defense.  Master of Science, Information Sciences, Naval Postgraduate School.

·       Opel, K. (9/2016). Unlocking the Secrets of Successful Software-Intensive Systems. Master of Science, Information Sciences, Naval Postgraduate School.

·       Reid, E. (6/2011). Social Network Collaboration for Crisis Response Operations: Developing a Situational Awareness (SA) Tool to Improve Haiti's Interagency Relief Efforts.  Master of Science, Information Sciences, Naval Postgraduate School.

Patents

·       Zhao, Y., (2016) "Multiple domain anomaly detection system and method using fusion rule and visualization," "9,323,837".

·       Zhao, Y., (2014) "System and method for knowledge pattern search from networked agents," "US Patent 8,903,756".

Workshops

·       fss17-dmai4ma

·       sss20-dmai4da